Publications by Year: 2024

Psychiatric trials have some of the lowest success rates across therapeutic areas, resulting in decreased investment in psychopharmacological drug development even as the need for more effective treatments grows. Digital measures and digital biomarkers (DBMs) provide one potential avenue for ameliorating three of the largest problems impeding clinical trial success in psychiatry: diagnostic heterogeneity, endpoint subjectivity, and high placebo response rates. First, DBMs may address heterogeneity and comorbidity in psychiatric nosology by identifying predictive DBMs of treatment response via the targeting of drugs to psychiatric subtypes. Second, DBMs can provide objective measures of physiology and behavior that when grounded in meaningful aspects of health (MAH) could support use for regulatory decision-making. By objectively and continuously measuring aspects of a patient's disease that the patient wants to improve or prevent from getting worse, DBMs might provide clinical trial endpoints that are more sensitive to treatment effects as compared to traditional clinician-reported outcomes. Lastly, DBMs could help address challenges surrounding high placebo response rates. Development of predictive DBMs of placebo response may allow for improved enrichment study designs to reduce placebo response. Objective digital measures may also be more robust against the placebo effect and offer an improved study endpoint alternative. Successful deployment of DBMs to address the historical challenges facing psychiatric drug trials will require close collaboration between industry, academic, and regulatory partners.

Stress produces profound effects on behavior, including persistent alterations in sleep patterns. Here we examined the effects of two prototypical stress peptides, pituitary adenylate cyclase-activating polypeptide (PACAP) and corticotropin-releasing factor (CRF), on sleep architecture and other translationally-relevant endpoints. Male and female mice were implanted with subcutaneous transmitters enabling continuous measurement of electroencephalography (EEG) and electromyography (EMG), as well as body temperature and locomotor activity, without tethering that restricts free movement, body posture, or head orientation during sleep. At baseline, females spent more time awake (AW) and less time in slow wave sleep (SWS) than males. Mice then received intracerebral infusions of PACAP or CRF at doses producing equivalent increases in anxiety-like behavior. The effects of PACAP on sleep architecture were similar in both sexes and resembled those reported in male mice after chronic stress exposure. Compared to vehicle infusions, PACAP infusions decreased time in AW, increased time in SWS, and increased rapid eye movement sleep (REM) time and bouts on the day following treatment. In addition, PACAP effects on REM time remained detectable a week after treatment. PACAP infusions also reduced body temperature and locomotor activity. Under the same experimental conditions, CRF infusions had minimal effects on sleep architecture in either sex, causing only transient increases in SWS during the dark phase, with no effects on temperature or activity. These findings suggest that PACAP and CRF have fundamentally different effects on sleep-related metrics and provide new insights into the mechanisms by which stress disrupts sleep.

Artificial intelligence (AI)-based computational tools for deriving digital behavioral markers are increasingly able to automatically detect clinically relevant patterns in mood and behavior through algorithmic analysis of continuously and passively collected data. The integration of these technologies into clinical care is imminent, most notably in clinical psychology and psychiatry but also other disciplines (e.g., cardiology, neurology, neurosurgery, pain management). Meanwhile, ethical guidelines for implementation are lacking, as are insights into what patients and caregivers want and need to know about these technologies to ensure acceptability and informed consent. In this work, we present qualitative findings from interviews with 40 adolescent patients and their caregivers examining ethical and practical considerations for translating these technologies into clinical care. We observed seven key domains (in order of salience) in stakeholders' informational needs: (1) clinical utility and value; (2) evidence, explainability, evaluation and contestation; (3) accuracy and trustworthiness; (4) data security, privacy, and misuse; (5) patient consent, control, and autonomy; (6) physician-patient relationship; and (7) patient safety, well-being, and dignity. Drawing from these themes, we provide a checklist of questions, as well as suggestions and key challenges, to help researchers and practitioners respond to what stakeholders want to know when integrating these technologies into clinical care and research. Our findings inform participatory approaches to co-designing treatment roadmaps for using these AI-based tools for enhanced patient engagement, acceptability and informed consent.

The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical data pivotal in clinical contexts, into LLM paradigms has not been thoroughly explored. In this study, we examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record (EHR) data. We compare the performance of these embeddings with that of raw numerical EHR data when used as feature inputs to traditional machine learning (ML) algorithms that excel at tabular data learning, such as eXtreme Gradient Boosting. We focus on instruction-tuned LLMs in a zero-shot setting to represent abnormal physiological data and evaluating their utilities as feature extractors to enhance ML classifiers for predicting diagnoses, length of stay, and mortality. Furthermore, we examine prompt engineering techniques on zero-shot and few-shot LLM embeddings to measure their impact comprehensively. Although findings suggest the raw data features still prevail in medical ML tasks, zero-shot LLM embeddings demonstrate competitive results, suggesting a promising avenue for future research in medical applications.

Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. This is particularly crucial for drug names, where patients often use brand names like Advil or Tylenol instead of their generic equivalents. To study this, we create a new robustness dataset, RABBITS, to evaluate performance differences on medical benchmarks after swapping brand and generic drug names using physician expert annotations. We assess both open-source and API-based LLMs on MedQA and MedMCQA, revealing a consistent performance drop ranging from 1-10%. Furthermore, we identify a potential source of this fragility as the contamination of test data in widely used pre-training datasets.

Although resilience is a dynamic process of recovery after trauma, in most studies it is conceptualized as the absence of specific psychopathology following trauma. Here, using the emergency department AURORA study (n = 1,835 with 63% women), we took a longitudinal, dynamic and transdiagnostic approach to define a static resilience (r) factor, which could explain greater than 50% of variance in mental well-being 6 months following trauma and a dynamic resilience factor, which represented recovery from initial symptoms. We then assessed its neurobiological profile across threat, inhibition and reward processes using functional magnetic resonance imaging collected 2 weeks post-trauma (n = 260). Our whole-brain and study-wide Bonferroni-corrected results suggest that resilience is promoted by activation of regions involved in higher-level cognitive functioning, reward valuation and salience detection in response to reward, whereas resilience is hampered by posterior default mode network activation to threat and reward. These findings serve to generate new hypotheses for brain mechanisms that could promote dynamic and multifaceted components of resilience following trauma.

BACKGROUND: Ankle fracture treatment is predicated on minimal displacement, leading to abnormal joint contact area. The purpose of this investigation is to determine whether computed tomography (CT) detects subtle mortise malalignment undetectable by x-ray in supination-external rotation-II (SER-II) injuries.

METHODS: A total of 24 patients with SER-II injuries, as demonstrated by negative gravity stress radiography, were included. Medial clear space (MCS) measurements were performed on bilateral ankle x-rays (injured and contralateral, uninjured side) at several time points as well as bilateral non-weight-bearing CT performed once clinical and radiographic healing was demonstrated (mean = 66 days post injury, range = 61-105 days). Statistical analyses examined differences in measurements between both sides.

RESULTS: Final x-rays demonstrated no differences between normal and injured ankle MCS (P = .441). However, CT coronal/axial MCS measurements were different (P < .05). CT coronal MCS measured wider by a mean difference of 0.67 mm (P < .001).

CONCLUSION: There is a high incidence of subtle mortise malalignment in SER-II ankle fractures, as demonstrated by CT, which is undetectable when assessed by plain radiographs. Although clinical outcomes are yet unknown, there are important implications of the finding of confirmed, subtle mortise malalignment in SER-II injuries and the limitations of x-ray to detect it.

LEVEL OF EVIDENCE: Level III.

PURPOSE: Rheumatoid arthritis (RA) can have severe impact on patients' functional abilities and increase the risk of fragility fractures. Little is known about how patients with RA fare after operative management of distal radius fractures. The purpose of this study was to compare postoperative complications after surgical fixation in patients with RA and controls, hypothesizing that patients with RA would have higher levels of postoperative complications.

METHODS: Patients were identified using Current Procedural Terminology and International Classification of Diseases, Ninth and Tenth Revision, codes for open treatment of distal radius fractures and RA at 3 level 1 trauma centers over a 5-year period (2015-2019). Chart abstraction provided details regarding injuries and treatment. Age- and sex-matched controls were identified in a 2:1 ratio. Postoperative complications were classified according to the Clavien-Dindo-Sink classification system and divided into early (less than 90 days) and late groups.

RESULTS: Sixty-four patients (21 with RA and 43 controls) were included. The patients were predominantly women, with a mean age of 62 years and a mean Charlson comorbidity index of 2.1. The RA medications at the time of injury included conventional synthetic disease-modifying antirheumatic drugs (5/21), biologic disease-modifying antirheumatic drugs (5/21), or chronic oral prednisone (6/21). Rheumatoid medications, except hydroxychloroquine, were withheld for 2-3 weeks after surgery. Rheumatoid patients were significantly more likely to sustain a complication compared with the control group, although this was no longer significant on adjusted analysis. Class I complications were the most common. The incidence of early versus late complications was similar between the groups. A high rate of early return to surgery for fixation failure occurred in the RA group compared with none in the control group.

CONCLUSIONS: Patients with RA undergoing operative management of distal radius fractures are at risk of postoperative complications, particularly fracture fixation failure, necessitating return to the operative room. High levels of pain, stiffness, and mechanical symptoms were noted in the RA group.

TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic IV.

PURPOSE: "Grit" is defined as the perseverance and passion for long-term goals. Thus, grittier patients may have a better function after common hand procedures; however, this is not well-documented in the literature. Our purpose was to assess the correlation between grit and self-reported physical function among patients undergoing open reduction internal fixation (ORIF) for distal radius fractures (DRFs).

METHODS: Between 2017 and 2020, patients undergoing ORIF for DRFs were identified. They were asked to complete the Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) questionnaire before surgery and at 6 weeks, 3 months, and 1 year after surgery. The first 100 patients with at least 1-year follow-up also completed the 8-question GRIT Scale, a validated measure of passion and perseverance for long-term goals measured on a scale of 0 (least grit) to 5 (most grit). The correlation between the QuickDASH and GRIT Scale scores was calculated using Spearman rho (ρ).

RESULTS: The average GRIT Scale score was 4.0 (SD, 0.7), with a median of 4.1 (range, 1.6-5.0). The median QuickDASH scores at the preoperative, 6-week postoperative, 6-month postoperative, and 1-year postoperative time points were 80 (range, 7-100), 43 (range, 2-100), 20 (range, 0-100), and 5 (range, 0-89), respectively. No significant correlation was found between the GRIT Scale and QuickDASH scores at any time.

CONCLUSIONS: We found no correlation between self-reported physical function and GRIT levels in patients undergoing ORIF for DRFs, suggesting no correlation between grit and patient-reported outcomes in this context. Future studies are needed to investigate the influence of individual differences in character traits other than grit on patient outcomes, which may help better align resources where needed and further the ability to deliver individualized, quality health care.

TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic IV.

BACKGROUND:  Older and frailer patients are increasingly undergoing free or pedicled tissue transfer for lower extremity (LE) limb salvage. This novel study examines the impact of frailty on postoperative outcomes in LE limb salvage patients undergoing free or pedicled tissue transfer.

METHODS:  The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (2010-2020) was queried for free and pedicled tissue transfer to the LE based on Current Procedural Terminology and the International Classification of Diseases9/10 codes. Demographic and clinical variables were extracted. The five-factor modified frailty index (mFI-5) was calculated using functional status, diabetes, chronic obstructive pulmonary disease, congestive heart failure, and hypertension. Patients were stratified by mFI-5 score: no frailty (0), intermediate frailty (1), and high frailty (2 + ). Univariate analysis and multivariate logistic regression were performed.

RESULTS:  In total, 5,196 patients underwent free or pedicled tissue transfer for LE limb salvage. A majority were intermediate (n = 1,977) or high (n = 1,466) frailty. High frailty patients had greater rates of comorbidities-including those not in the mFI-5 score. Higher frailty was associated with more systemic and all-cause complications. On multivariate analysis, the mFI-5 score remained the best predictor of all-cause complications-with high frailty associated with 1.74 increased adjusted odds when compared with no frailty (95% confidence interval: 1.47-2.05).

CONCLUSION:  While flap type, age, and diagnosis were independent predictors of outcomes in LE flap reconstruction, frailty (mFI-5) was the strongest predictor on adjusted analysis. This study validates the mFI-5 score for preoperative risk assessment for flap procedures in LE limb salvage. These results highlight the likely importance of prehabilitation and medical optimization prior to limb salvage.